{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3418a970",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 逻辑回归 ===\n",
      "准确率: 0.8300\n",
      "AUC分数: 0.9009\n",
      "\n",
      "=== 随机森林 ===\n",
      "准确率: 0.8400\n",
      "AUC分数: 0.9428\n",
      "\n",
      "=== 梯度提升树 ===\n",
      "准确率: 0.8450\n",
      "AUC分数: 0.9573\n",
      "\n",
      "=== SVM ===\n",
      "准确率: 0.7550\n",
      "AUC分数: 0.8542\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 模型性能比较 ===\n",
      "      模型    准确率     AUC分数\n",
      "2  梯度提升树  0.845  0.957278\n",
      "1   随机森林  0.840  0.942834\n",
      "0   逻辑回归  0.830  0.900926\n",
      "3    SVM  0.755  0.854186\n",
      "\n",
      "=== 特征重要性分析 ===\n",
      "随机森林特征重要性:\n",
      "      特征名称       重要性\n",
      "4  月消费/月收入  0.349311\n",
      "2   月消费（元）  0.277845\n",
      "0       年龄  0.208996\n",
      "1   月收入（元）  0.139214\n",
      "3       性别  0.024635\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "梯度提升树特征重要性:\n",
      "      特征名称       重要性\n",
      "4  月消费/月收入  0.452878\n",
      "2   月消费（元）  0.288788\n",
      "0       年龄  0.221418\n",
      "1   月收入（元）  0.020542\n",
      "3       性别  0.016373\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 逻辑回归系数分析 ===\n",
      "      特征名称        系数     系数绝对值\n",
      "3       性别 -0.691224  0.691224\n",
      "0       年龄 -0.111026  0.111026\n",
      "4  月消费/月收入  0.065984  0.065984\n",
      "2   月消费（元）  0.000942  0.000942\n",
      "1   月收入（元） -0.000375  0.000375\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 最佳模型: 梯度提升树 ===\n",
      "最佳AUC分数: 0.9573\n",
      "最佳模型的前5个预测概率: [0.98451965 0.9732452  0.96898689 0.02636972 0.94179565]\n",
      "\n",
      "=== 模型预测结果对比 ===\n",
      "   实际值  逻辑回归预测概率  随机森林预测概率  梯度提升树预测概率   SVM预测概率\n",
      "0    1  0.685676      1.00   0.984520  0.408411\n",
      "1    1  0.571732      1.00   0.973245  0.711623\n",
      "2    1  0.910436      1.00   0.968987  0.845806\n",
      "3    0  0.224482      0.22   0.026370  0.301293\n",
      "4    1  0.617899      0.97   0.941796  0.568677\n",
      "5    0  0.090170      0.00   0.026965  0.211862\n",
      "6    1  0.958557      0.97   0.974220  0.928480\n",
      "7    0  0.381457      0.35   0.286092  0.254184\n",
      "8    0  0.800427      0.41   0.368891  0.704440\n",
      "9    0  0.096019      0.02   0.024963  0.097643\n"
     ]
    }
   ],
   "source": [
    "# 设置中文字体支持（添加在代码开头）\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "# 1.读取数据\n",
    "import pandas as pd\n",
    "df = pd.read_excel(\"D:\\贺之恒的python\\pppython\\python商业数据分析\\@Python大数据分析与机器学习商业案例实战\\第9章 AdaBoost与GBDT模型\\源代码汇总_Pycharm\\信用卡精准营销模型.xlsx\")\n",
    "df.head()\n",
    "\n",
    "# 2.提取特征变量和目标变量\n",
    "X = df.drop(columns='响应')\n",
    "y = df['响应']\n",
    "\n",
    "# 3.划分训练集和测试集\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)\n",
    "\n",
    "# 4.导入多种模型\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score, roc_curve\n",
    "\n",
    "# 创建模型字典\n",
    "models = {\n",
    "    '逻辑回归': LogisticRegression(random_state=123),\n",
    "    '随机森林': RandomForestClassifier(random_state=123),\n",
    "    '梯度提升树': GradientBoostingClassifier(random_state=123),\n",
    "    'SVM': SVC(probability=True, random_state=123)  # probability=True用于获取概率预测\n",
    "}\n",
    "\n",
    "# 存储结果\n",
    "results = {}\n",
    "\n",
    "# 5.训练和评估各个模型\n",
    "for model_name, model in models.items():\n",
    "    print(f\"\\n=== {model_name} ===\")\n",
    "    \n",
    "    # 模型训练\n",
    "    model.fit(X_train, y_train)\n",
    "    \n",
    "    # 模型预测\n",
    "    y_pred = model.predict(X_test)\n",
    "    y_pred_proba = model.predict_proba(X_test)[:, 1]\n",
    "    \n",
    "    # 评估指标\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    auc_score = roc_auc_score(y_test, y_pred_proba)\n",
    "    \n",
    "    print(f\"准确率: {accuracy:.4f}\")\n",
    "    print(f\"AUC分数: {auc_score:.4f}\")\n",
    "    \n",
    "    # 存储结果\n",
    "    results[model_name] = {\n",
    "        'model': model,\n",
    "        'accuracy': accuracy,\n",
    "        'auc': auc_score,\n",
    "        'y_pred_proba': y_pred_proba\n",
    "    }\n",
    "\n",
    "# 6.绘制所有模型的ROC曲线\n",
    "plt.figure(figsize=(10, 8))\n",
    "for model_name, result in results.items():\n",
    "    fpr, tpr, _ = roc_curve(y_test, result['y_pred_proba'])\n",
    "    plt.plot(fpr, tpr, linewidth=2, label=f'{model_name} (AUC = {result[\"auc\"]:.3f})')\n",
    "\n",
    "plt.plot([0, 1], [0, 1], 'k--', label='随机分类器')\n",
    "plt.xlabel('假正率 (False Positive Rate)')\n",
    "plt.ylabel('真正率 (True Positive Rate)')\n",
    "plt.title('多种模型的ROC曲线比较')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 7.比较模型性能\n",
    "print(\"\\n=== 模型性能比较 ===\")\n",
    "performance_df = pd.DataFrame({\n",
    "    '模型': list(results.keys()),\n",
    "    '准确率': [result['accuracy'] for result in results.values()],\n",
    "    'AUC分数': [result['auc'] for result in results.values()]\n",
    "}).sort_values('AUC分数', ascending=False)\n",
    "\n",
    "print(performance_df)\n",
    "\n",
    "# 8.特征重要性分析（适用于树模型）\n",
    "print(\"\\n=== 特征重要性分析 ===\")\n",
    "\n",
    "# 随机森林的特征重要性\n",
    "rf_model = results['随机森林']['model']\n",
    "rf_importances = pd.DataFrame({\n",
    "    '特征名称': X.columns,\n",
    "    '重要性': rf_model.feature_importances_\n",
    "}).sort_values('重要性', ascending=False)\n",
    "\n",
    "print(\"随机森林特征重要性:\")\n",
    "print(rf_importances)\n",
    "\n",
    "# 可视化特征重要性\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.barh(rf_importances['特征名称'][:10], rf_importances['重要性'][:10])\n",
    "plt.xlabel('特征重要性')\n",
    "plt.title('随机森林 - 前10重要特征')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 梯度提升树的特征重要性\n",
    "gbdt_model = results['梯度提升树']['model']\n",
    "gbdt_importances = pd.DataFrame({\n",
    "    '特征名称': X.columns,\n",
    "    '重要性': gbdt_model.feature_importances_\n",
    "}).sort_values('重要性', ascending=False)\n",
    "\n",
    "print(\"\\n梯度提升树特征重要性:\")\n",
    "print(gbdt_importances)\n",
    "\n",
    "# 可视化梯度提升树特征重要性\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.barh(gbdt_importances['特征名称'][:10], gbdt_importances['重要性'][:10])\n",
    "plt.xlabel('特征重要性')\n",
    "plt.title('梯度提升树 - 前10重要特征')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 9.逻辑回归的系数分析\n",
    "print(\"\\n=== 逻辑回归系数分析 ===\")\n",
    "lr_model = results['逻辑回归']['model']\n",
    "lr_coefficients = pd.DataFrame({\n",
    "    '特征名称': X.columns,\n",
    "    '系数': lr_model.coef_[0],\n",
    "    '系数绝对值': abs(lr_model.coef_[0])\n",
    "}).sort_values('系数绝对值', ascending=False)\n",
    "\n",
    "print(lr_coefficients)\n",
    "\n",
    "# 可视化逻辑回归系数\n",
    "plt.figure(figsize=(10, 6))\n",
    "top_coeff = lr_coefficients.head(10).sort_values('系数', ascending=True)\n",
    "plt.barh(top_coeff['特征名称'], top_coeff['系数'])\n",
    "plt.xlabel('系数值')\n",
    "plt.title('逻辑回归 - 前10重要特征系数')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 10.选择最佳模型\n",
    "best_model_name = performance_df.iloc[0]['模型']\n",
    "best_model = results[best_model_name]['model']\n",
    "best_auc = performance_df.iloc[0]['AUC分数']\n",
    "\n",
    "print(f\"\\n=== 最佳模型: {best_model_name} ===\")\n",
    "print(f\"最佳AUC分数: {best_auc:.4f}\")\n",
    "\n",
    "# 使用最佳模型进行预测示例\n",
    "best_y_pred_proba = best_model.predict_proba(X_test)[:, 1]\n",
    "print(\"最佳模型的前5个预测概率:\", best_y_pred_proba[:5])\n",
    "\n",
    "# 11.添加模型预测结果对比\n",
    "print(\"\\n=== 模型预测结果对比 ===\")\n",
    "comparison_df = pd.DataFrame({\n",
    "    '实际值': y_test.values,\n",
    "    '逻辑回归预测概率': results['逻辑回归']['y_pred_proba'],\n",
    "    '随机森林预测概率': results['随机森林']['y_pred_proba'],\n",
    "    '梯度提升树预测概率': results['梯度提升树']['y_pred_proba'],\n",
    "    'SVM预测概率': results['SVM']['y_pred_proba']\n",
    "})\n",
    "\n",
    "print(comparison_df.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe22b11a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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